Person　re-identification　(re-id)　has　attracted　widespread　attention　due　to　its　application　and　research　significance.　However,　since　the　person　re-id　puts　the　cropped　images　as　input,　it　is　far　from　the　real-world　scenarios　just　like　person　search　which　aims　at　matching　a　target　person　from　a　gallery　of　the　whole　scene　images.　Person　search　is　more　difficult　but　more　practical　and　meaningful.　In　this　paper,　we　propose　a　new　person　search　network　with　an　enhanced　feature　representation.　Our　network　mainly　consists　two　parts,　a　pedestrian　proposal　net　and　an　identification　net.　In　the　identification　net,　we　utilize　hand-crafted　features　and　Convolutional　Neural　Network　(CNN)　features　to　get　more　discriminative　and　compact　features.　Experiments　on　a　large-scale　benchmark　dataset　demonstrate　our　network　gets　better　performance　than　others　counterparts.